INFORMATION THEORY AND CRITTOGRAPHY
Knowledge and understanding:
Knowledge of entropy and mutual information functions and their properties. Knowledge of results and techniques for source and channel coding.
Applying knowledge and understanding:
Capability to apply the main results on entropy and mutual information. capability to apply the main results of source and channel coding to communications problems. Capability to implement in Matlab/Octave the results.
Capability to choose the best method to analyze problems of source and channel coding.
Communication skills: Capability to present a topic of the course.
Capability to elaborate and synthesize and implement in Matlab/Octave the course contents.
Contents of the courses Random Phenomena end Electrical Communications.
Theory 50%, Numeric exercises 17%, Matlab/Octave exercises 33%
Entropy, relative entropy and mutual information. Asymptotic equipartition property. Entropy rate of a stochastic process. Data compression. Channel capacity. Channel coding. Differential entropy. Gaussian channel. Cryptography. Matlab/Octave exercises.
Lectures; Class Exercises; Matlab/Octave exercises in laboratory.
 T. M. Cover and J. A. Thomas, Elements of Information Theory. Wiley, New York, 1991.
 R. B. Ash, Information Theory. Dover, New York, 1990.
 S. Benedetto, E. Biglieri, Principles of Digital Transmission With Wireless Applications, Kluwer, 1999. R. L. Rivest, A. Shamir, and L. Adleman, On digital signatures and public-key cryptosystems, MIT Technical Report MIT/LCS/TM-82, April 1977.
Discussion of Matlab/Octave exercises and oral exam.